Overview

Dataset statistics

Number of variables12
Number of observations911
Missing cells164
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.5 KiB
Average record size in memory96.1 B

Variable types

Numeric8
DateTime1
Categorical3

Alerts

food_waste_kg is highly overall correlated with meals_served and 1 other fieldsHigh correlation
meals_served is highly overall correlated with food_waste_kgHigh correlation
past_waste_kg is highly overall correlated with food_waste_kgHigh correlation
special_event is highly imbalanced (57.8%)Imbalance
staff_experience has 164 (18.0%) missing valuesMissing
ID has unique valuesUnique
day_of_week has 131 (14.4%) zerosZeros

Reproduction

Analysis started2025-12-26 06:42:46.462728
Analysis finished2025-12-26 06:43:02.843352
Duration16.38 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Unique 

Distinct911
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean528.32711
Minimum0
Maximum1049
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-12-26T09:43:03.265407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48.5
Q1266
median531
Q3795.5
95-th percentile999.5
Maximum1049
Range1049
Interquartile range (IQR)529.5

Descriptive statistics

Standard deviation305.07279
Coefficient of variation (CV)0.57743164
Kurtosis-1.1980013
Mean528.32711
Median Absolute Deviation (MAD)265
Skewness-0.01574909
Sum481306
Variance93069.409
MonotonicityStrictly increasing
2025-12-26T09:43:03.464302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
7291
 
0.1%
6961
 
0.1%
6971
 
0.1%
6981
 
0.1%
7001
 
0.1%
7031
 
0.1%
7041
 
0.1%
7051
 
0.1%
7061
 
0.1%
Other values (901)901
98.9%
ValueCountFrequency (%)
01
0.1%
11
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
111
0.1%
ValueCountFrequency (%)
10491
0.1%
10481
0.1%
10461
0.1%
10451
0.1%
10441
0.1%
10431
0.1%
10421
0.1%
10411
0.1%
10401
0.1%
10391
0.1%

date
Date

Distinct867
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
Minimum2022-01-01 00:00:00
Maximum2024-09-26 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-26T09:43:03.674874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:43:03.902977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

meals_served
Real number (ℝ)

High correlation 

Distinct373
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375.40505
Minimum100
Maximum4730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-12-26T09:43:04.092975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile121.5
Q1211
median306
Q3407
95-th percentile488.5
Maximum4730
Range4630
Interquartile range (IQR)196

Descriptive statistics

Standard deviation502.81272
Coefficient of variation (CV)1.3393872
Kurtosis46.409098
Mean375.40505
Median Absolute Deviation (MAD)100
Skewness6.5811275
Sum341994
Variance252820.63
MonotonicityNot monotonic
2025-12-26T09:43:04.327997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3527
 
0.8%
1987
 
0.8%
2597
 
0.8%
4857
 
0.8%
2917
 
0.8%
3766
 
0.7%
2896
 
0.7%
1506
 
0.7%
1536
 
0.7%
2436
 
0.7%
Other values (363)846
92.9%
ValueCountFrequency (%)
1004
0.4%
1011
 
0.1%
1021
 
0.1%
1032
 
0.2%
1045
0.5%
1051
 
0.1%
1061
 
0.1%
1072
 
0.2%
1082
 
0.2%
1092
 
0.2%
ValueCountFrequency (%)
47301
0.1%
46801
0.1%
45902
0.2%
45101
0.1%
41001
0.1%
40802
0.2%
40101
0.1%
35801
0.1%
33801
0.1%
31901
0.1%

kitchen_staff
Real number (ℝ)

Distinct15
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.90011
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-12-26T09:43:04.486737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18
median12
Q315
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2851533
Coefficient of variation (CV)0.36009359
Kurtosis-1.1502364
Mean11.90011
Median Absolute Deviation (MAD)4
Skewness0.020201992
Sum10841
Variance18.362538
MonotonicityNot monotonic
2025-12-26T09:43:04.643337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1272
 
7.9%
569
 
7.6%
1068
 
7.5%
1364
 
7.0%
964
 
7.0%
1563
 
6.9%
1861
 
6.7%
1160
 
6.6%
759
 
6.5%
1459
 
6.5%
Other values (5)272
29.9%
ValueCountFrequency (%)
569
7.6%
657
6.3%
759
6.5%
851
5.6%
964
7.0%
1068
7.5%
1160
6.6%
1272
7.9%
1364
7.0%
1459
6.5%
ValueCountFrequency (%)
1958
6.4%
1861
6.7%
1754
5.9%
1652
5.7%
1563
6.9%
1459
6.5%
1364
7.0%
1272
7.9%
1160
6.6%
1068
7.5%

temperature_C
Real number (ℝ)

Distinct892
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.18928
Minimum-10.372207
Maximum60
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)1.4%
Memory size7.2 KiB
2025-12-26T09:43:04.872453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-10.372207
5-th percentile10.758709
Q115.684585
median22.11504
Q328.804294
95-th percentile33.976293
Maximum60
Range70.372207
Interquartile range (IQR)13.119709

Descriptive statistics

Standard deviation8.9223894
Coefficient of variation (CV)0.40210359
Kurtosis3.3911836
Mean22.18928
Median Absolute Deviation (MAD)6.5401418
Skewness0.11185402
Sum20214.434
Variance79.609032
MonotonicityNot monotonic
2025-12-26T09:43:05.071961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1012
 
1.3%
609
 
1.0%
27.887272681
 
0.1%
21.981973211
 
0.1%
31.145476341
 
0.1%
26.899556891
 
0.1%
13.445572481
 
0.1%
23.949518471
 
0.1%
12.33519931
 
0.1%
29.656898921
 
0.1%
Other values (882)882
96.8%
ValueCountFrequency (%)
-10.372206511
 
0.1%
-1012
1.3%
9.7833931731
 
0.1%
9.8656589561
 
0.1%
10.008312651
 
0.1%
10.0257371
 
0.1%
10.028002791
 
0.1%
10.030795881
 
0.1%
10.030916991
 
0.1%
10.046997091
 
0.1%
ValueCountFrequency (%)
609
1.0%
35.116763711
 
0.1%
34.887012271
 
0.1%
34.833423431
 
0.1%
34.810385011
 
0.1%
34.803332911
 
0.1%
34.789710221
 
0.1%
34.77816081
 
0.1%
34.753709321
 
0.1%
34.710399741
 
0.1%

humidity_percent
Real number (ℝ)

Distinct867
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.761313
Minimum30.121111
Maximum89.982828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-12-26T09:43:05.285064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30.121111
5-th percentile32.656491
Q146.017835
median61.63396
Q375.78791
95-th percentile86.938743
Maximum89.982828
Range59.861717
Interquartile range (IQR)29.770075

Descriptive statistics

Standard deviation17.330821
Coefficient of variation (CV)0.28522789
Kurtosis-1.1568695
Mean60.761313
Median Absolute Deviation (MAD)15.209952
Skewness-0.093233569
Sum55353.556
Variance300.35736
MonotonicityNot monotonic
2025-12-26T09:43:05.492946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.229028052
 
0.2%
84.216470662
 
0.2%
78.443761822
 
0.2%
43.603822982
 
0.2%
54.765667762
 
0.2%
50.422805562
 
0.2%
52.896716392
 
0.2%
40.144864222
 
0.2%
78.141259752
 
0.2%
53.385563252
 
0.2%
Other values (857)891
97.8%
ValueCountFrequency (%)
30.121111061
0.1%
30.126230591
0.1%
30.139052871
0.1%
30.17287511
0.1%
30.229028052
0.2%
30.339948871
0.1%
30.365324891
0.1%
30.400927751
0.1%
30.402623421
0.1%
30.60543641
0.1%
ValueCountFrequency (%)
89.982828251
0.1%
89.972635121
0.1%
89.967974891
0.1%
89.775737191
0.1%
89.767376981
0.1%
89.698882161
0.1%
89.691478381
0.1%
89.546537361
0.1%
89.504557032
0.2%
89.47579361
0.1%

day_of_week
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.01427
Minimum0
Maximum6
Zeros131
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-12-26T09:43:05.634407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0095417
Coefficient of variation (CV)0.66667606
Kurtosis-1.2597198
Mean3.01427
Median Absolute Deviation (MAD)2
Skewness-0.0081864664
Sum2746
Variance4.0382577
MonotonicityNot monotonic
2025-12-26T09:43:05.767912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6133
14.6%
5133
14.6%
2132
14.5%
0131
14.4%
3128
14.1%
1127
13.9%
4127
13.9%
ValueCountFrequency (%)
0131
14.4%
1127
13.9%
2132
14.5%
3128
14.1%
4127
13.9%
5133
14.6%
6133
14.6%
ValueCountFrequency (%)
6133
14.6%
5133
14.6%
4127
13.9%
3128
14.1%
2132
14.5%
1127
13.9%
0131
14.4%

special_event
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
0
833 
1
 
78

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters911
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0833
91.4%
178
 
8.6%

Length

2025-12-26T09:43:05.922027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-26T09:43:06.080501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0833
91.4%
178
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0833
91.4%
178
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0833
91.4%
178
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0833
91.4%
178
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0833
91.4%
178
 
8.6%

past_waste_kg
Real number (ℝ)

High correlation 

Distinct867
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.015691
Minimum5.0083938
Maximum49.803703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-12-26T09:43:06.235814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.0083938
5-th percentile7.4520044
Q116.091383
median26.854109
Q338.149878
95-th percentile46.944205
Maximum49.803703
Range44.795309
Interquartile range (IQR)22.058494

Descriptive statistics

Standard deviation12.774223
Coefficient of variation (CV)0.47284457
Kurtosis-1.205059
Mean27.015691
Median Absolute Deviation (MAD)11.044234
Skewness0.016267647
Sum24611.294
Variance163.18076
MonotonicityNot monotonic
2025-12-26T09:43:06.438981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.337528982
 
0.2%
38.232764532
 
0.2%
10.38028992
 
0.2%
39.537447382
 
0.2%
40.842883392
 
0.2%
19.401229252
 
0.2%
18.83884082
 
0.2%
9.1625197072
 
0.2%
15.464359922
 
0.2%
40.115653822
 
0.2%
Other values (857)891
97.8%
ValueCountFrequency (%)
5.0083937681
0.1%
5.0418237591
0.1%
5.073969071
0.1%
5.1726715481
0.1%
5.2312832411
0.1%
5.3066614061
0.1%
5.4722138511
0.1%
5.474800511
0.1%
5.4985348671
0.1%
5.5553470731
0.1%
ValueCountFrequency (%)
49.803702511
0.1%
49.796336861
0.1%
49.733674461
0.1%
49.720278891
0.1%
49.696933841
0.1%
49.635013821
0.1%
49.497598912
0.2%
49.484299421
0.1%
49.448729261
0.1%
49.395340141
0.1%

staff_experience
Categorical

Missing 

Distinct4
Distinct (%)0.5%
Missing164
Missing (%)18.0%
Memory size7.2 KiB
Beginner
191 
Intermediate
186 
EXPERT
186 
intermediate
184 

Length

Max length12
Median length8
Mean length9.4832664
Min length6

Characters and Unicode

Total characters7084
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowintermediate
2nd rowBeginner
3rd rowBeginner
4th rowIntermediate
5th rowIntermediate

Common Values

ValueCountFrequency (%)
Beginner191
21.0%
Intermediate186
20.4%
EXPERT186
20.4%
intermediate184
20.2%
(Missing)164
18.0%

Length

2025-12-26T09:43:06.705328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-26T09:43:06.853559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
intermediate370
49.5%
beginner191
25.6%
expert186
24.9%

Most occurring characters

ValueCountFrequency (%)
e1492
21.1%
n752
10.6%
i745
10.5%
t740
10.4%
r561
 
7.9%
E372
 
5.3%
m370
 
5.2%
d370
 
5.2%
a370
 
5.2%
B191
 
2.7%
Other values (6)1121
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)7084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1492
21.1%
n752
10.6%
i745
10.5%
t740
10.4%
r561
 
7.9%
E372
 
5.3%
m370
 
5.2%
d370
 
5.2%
a370
 
5.2%
B191
 
2.7%
Other values (6)1121
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1492
21.1%
n752
10.6%
i745
10.5%
t740
10.4%
r561
 
7.9%
E372
 
5.3%
m370
 
5.2%
d370
 
5.2%
a370
 
5.2%
B191
 
2.7%
Other values (6)1121
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1492
21.1%
n752
10.6%
i745
10.5%
t740
10.4%
r561
 
7.9%
E372
 
5.3%
m370
 
5.2%
d370
 
5.2%
a370
 
5.2%
B191
 
2.7%
Other values (6)1121
15.8%

waste_category
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
MEAT
210 
dairy
180 
Vegetables
176 
GRAINS
176 
MeAt
169 

Length

Max length10
Median length6
Mean length5.7431394
Min length4

Characters and Unicode

Total characters5232
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdairy
2nd rowMeAt
3rd rowMeAt
4th rowMeAt
5th rowMEAT

Common Values

ValueCountFrequency (%)
MEAT210
23.1%
dairy180
19.8%
Vegetables176
19.3%
GRAINS176
19.3%
MeAt169
18.6%

Length

2025-12-26T09:43:07.002088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-26T09:43:07.125341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
meat379
41.6%
dairy180
19.8%
vegetables176
19.3%
grains176
19.3%

Most occurring characters

ValueCountFrequency (%)
e697
 
13.3%
A555
 
10.6%
M379
 
7.2%
a356
 
6.8%
t345
 
6.6%
E210
 
4.0%
T210
 
4.0%
r180
 
3.4%
y180
 
3.4%
i180
 
3.4%
Other values (11)1940
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e697
 
13.3%
A555
 
10.6%
M379
 
7.2%
a356
 
6.8%
t345
 
6.6%
E210
 
4.0%
T210
 
4.0%
r180
 
3.4%
y180
 
3.4%
i180
 
3.4%
Other values (11)1940
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e697
 
13.3%
A555
 
10.6%
M379
 
7.2%
a356
 
6.8%
t345
 
6.6%
E210
 
4.0%
T210
 
4.0%
r180
 
3.4%
y180
 
3.4%
i180
 
3.4%
Other values (11)1940
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e697
 
13.3%
A555
 
10.6%
M379
 
7.2%
a356
 
6.8%
t345
 
6.6%
E210
 
4.0%
T210
 
4.0%
r180
 
3.4%
y180
 
3.4%
i180
 
3.4%
Other values (11)1940
37.1%

food_waste_kg
Real number (ℝ)

High correlation 

Distinct867
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.842691
Minimum10.819048
Maximum274.32878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-12-26T09:43:07.339496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.819048
5-th percentile21.433718
Q132.887912
median41.14693
Q350.046681
95-th percentile64.089501
Maximum274.32878
Range263.50974
Interquartile range (IQR)17.158769

Descriptive statistics

Standard deviation27.934366
Coefficient of variation (CV)0.62294135
Kurtosis30.380443
Mean44.842691
Median Absolute Deviation (MAD)8.4422266
Skewness4.9909
Sum40851.691
Variance780.32882
MonotonicityNot monotonic
2025-12-26T09:43:07.525902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.106297762
 
0.2%
51.13027262
 
0.2%
42.437798292
 
0.2%
49.143667932
 
0.2%
30.738561362
 
0.2%
172.15558232
 
0.2%
24.449654562
 
0.2%
22.986920782
 
0.2%
35.122500252
 
0.2%
53.440622012
 
0.2%
Other values (857)891
97.8%
ValueCountFrequency (%)
10.819047911
0.1%
11.181281621
0.1%
13.960702111
0.1%
14.928976611
0.1%
15.029167731
0.1%
16.539617671
0.1%
16.566751781
0.1%
16.806291741
0.1%
17.158902221
0.1%
17.199561541
0.1%
ValueCountFrequency (%)
274.3287831
0.1%
256.32552361
0.1%
251.53893251
0.1%
249.17387761
0.1%
246.35916931
0.1%
238.88775551
0.1%
236.74080411
0.1%
234.78556721
0.1%
205.02305071
0.1%
201.32722651
0.1%

Interactions

2025-12-26T09:43:00.473782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-26T09:42:53.067811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:54.667430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:56.154243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:57.603835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:58.923485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:43:00.645343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:50.342423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:51.791810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:53.238423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:54.819507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-26T09:42:59.095299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:43:00.790813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:50.598091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-26T09:42:53.387580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:55.078724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:56.481578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-26T09:42:57.387738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:42:58.782859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-26T09:43:00.320347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-26T09:43:07.675794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
IDday_of_weekfood_waste_kghumidity_percentkitchen_staffmeals_servedpast_waste_kgspecial_eventstaff_experiencetemperature_Cwaste_category
ID1.0000.0290.0300.005-0.029-0.0400.0400.0830.0540.0490.000
day_of_week0.0291.0000.0030.0140.0340.002-0.0010.0000.0480.0240.065
food_waste_kg0.0300.0031.0000.0580.0310.5580.6510.2590.0000.0180.040
humidity_percent0.0050.0140.0581.000-0.021-0.0350.0150.0000.0000.0040.013
kitchen_staff-0.0290.0340.031-0.0211.000-0.026-0.0780.0000.000-0.0120.000
meals_served-0.0400.0020.558-0.035-0.0261.0000.0340.0540.064-0.0130.000
past_waste_kg0.040-0.0010.6510.015-0.0780.0341.0000.0000.052-0.0120.000
special_event0.0830.0000.2590.0000.0000.0540.0001.0000.0450.0000.000
staff_experience0.0540.0480.0000.0000.0000.0640.0520.0451.0000.0000.033
temperature_C0.0490.0240.0180.004-0.012-0.013-0.0120.0000.0001.0000.013
waste_category0.0000.0650.0400.0130.0000.0000.0000.0000.0330.0131.000

Missing values

2025-12-26T09:43:02.163422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-26T09:43:02.410451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDdatemeals_servedkitchen_stafftemperature_Chumidity_percentday_of_weekspecial_eventpast_waste_kgstaff_experiencewaste_categoryfood_waste_kg
002022-12-191961327.88727345.362854007.740587intermediatedairy28.946465
112023-11-212441510.31787264.4304751042.311779NaNMeAt51.549053
242022-02-011481627.71430069.0461131041.184305BeginnerMeAt53.008323
352023-03-191571919.17390246.2928236041.543492BeginnerMeAt48.621527
462022-07-182971026.37523379.7410640026.525097IntermediateMEAT44.156984
572023-03-022411816.86350679.2859193011.834878Intermediatedairy27.393670
682022-04-184431619.88862777.3281360022.862659BeginnerVegetables52.172118
792023-12-164161618.55959175.7865025134.599442IntermediateMeAt72.052407
8102023-07-074391824.11102743.3958034017.459149EXPERTdairy44.284157
9112023-11-07267725.41249389.4051831023.067392EXPERTGRAINS33.233930
IDdatemeals_servedkitchen_stafftemperature_Chumidity_percentday_of_weekspecial_eventpast_waste_kgstaff_experiencewaste_categoryfood_waste_kg
90110392024-03-16338933.75781937.1324635022.121685EXPERTMeAt34.751028
90210402022-12-103291030.34415778.3953615019.357028Intermediatedairy43.910552
90310412024-02-094151128.08511975.748973408.919918NaNMeAt41.321267
90410422022-05-11198618.94503458.1846262018.296057EXPERTGRAINS25.533362
90510432024-05-21202914.77976876.8597541041.520740intermediateMeAt42.887283
90610442022-03-293951817.35419945.1384351040.550668IntermediateGRAINS50.369152
90710452022-11-274831124.91213759.4850916036.470276intermediatedairy43.070121
90810462023-04-122431128.87094570.5084042019.767203IntermediateMeAt29.632560
90910482022-02-144061019.06163855.2866420028.560361EXPERTMEAT44.615759
91010492024-05-10350623.70801551.3512864046.786860intermediateVegetables57.066481